S1: All right. Welcome to episode 45. Posted a bunch of
S1: fabric extractions of stuff that I've been watching and reading
S1: and stuff like that. So like YouTube videos, blog posts,
S1: academic papers, and I think I'm going to do more
S1: of this because I'm actually building this into my Pi,
S1: which is pi personal AI infrastructure. And this is the
S1: infrastructure you've heard me talking about in the augmented class.
S1: And just like all over X and in the newsletter
S1: over the years, this is basically the back end core
S1: infrastructure that I basically runs for my life. So it's
S1: basically finding all my content. It's categorizing all my content, labeling, sorting,
S1: doing things like that, extracting summaries, um, you know, characterizing.
S1: And I'm basically going to use that to continue enhancing
S1: my life. Right. So I'm always adding to this thing.
S1: So one of the core components of that is actually
S1: finding new content, um, in an automated way. So basically
S1: there's I'll give you a good example. There's on arXiv.
S1: I think that's the way you pronounce this. I've heard
S1: people say this, I used to call it arXiv because whatever.
S1: I'm silly. arXiv, I guess it's called arXiv. It's a
S1: cool name. It's a better name than arXiv, although that's
S1: how it's spelled. But whatever on arXiv there is a
S1: RSS feed for. CS I so it's papers coming out
S1: of arXiv that are both CS related and AI related.
S1: And obviously this is a sweet spot for me. So
S1: I want to be able to go and get those papers.
S1: But I also want to know how high quality they are, like,
S1: what are the interesting claims that they're making? Like how
S1: good was their methodology, their testing? Like, was it a
S1: meta study? Did they do their own independent research for
S1: the paper, like all those combinations. And there's already a
S1: fabric pattern for this, which is analyze paper. And I
S1: think I have a new version of it as well.
S1: But anyway, I want to do that type of automated
S1: analysis on every paper I might want to read. Okay.
S1: This is kind of giving you hints of like what
S1: my personal AI infrastructure does. And by the way, I'm
S1: aware of the fact that I'm saying like a little
S1: bit too much. So I'm trying to tamper that down.
S1: I like the word. I think it's a cool transition word,
S1: but I don't want to use it 40 times in
S1: every sentence. Anyway, this is the infrastructure that I'm actually building,
S1: and this is one of the modules that's really, really
S1: important to me because it's not just papers. I want
S1: to see this for open source intelligence. I want to
S1: see this for all the different stuff. So these are
S1: all the independent modules that I'm building. And actually those
S1: essentially turn into products for me. Right. So my Intel
S1: product is going to be the output of one of these.
S1: Threshold is already the output of several of these, right?
S1: So anyway, that's just a brief little thing on my pie. Pie.
S1: All that to say that these fabric extractions are essentially
S1: outputs from these modules where it's found a piece of
S1: really unique content, or I manually found it and I
S1: just manually ran, uh, fabric against it. And it outputted
S1: this output so you could check it on X, you
S1: could see it on, you know, it. I do a
S1: few of these in the newsletter every once in a while,
S1: but I find it really useful to go and extract predictions,
S1: to extract wisdom, to extract insights, or is another one
S1: of my favorite. There's also one that just extracts quotes,
S1: which can also be really interesting. There's um, I think
S1: there's a one that just extracts like data or recommendations.
S1: I think there's definitely a recommendations. One, actually, now that
S1: I think about it, I need to have one that
S1: extracts like a book list or a watch list. Um,
S1: I'm not sure if I already made that one or
S1: if I need to go add that to fabric. Anyway,
S1: all this to say, I want my pi Pi always
S1: running in the background, always collecting, always finding, always discovering,
S1: always rating. And before you think, well hold on, I
S1: is doing too much work for you. No, it is
S1: not doing too much work for me. And here's why.
S1: Part of this entire thing is the determination of what
S1: I should go and do slowly. Okay? Anything that makes
S1: it into that bucket, I must sit down and manually
S1: enjoy and listen to and process and use my pen
S1: and my index cards and write down notes or Apple
S1: notes or whatever it is if I have to pause
S1: this thing. But I still think the absolute best way
S1: to experience anything is in the slow form, right? What's
S1: still the best possible way to spend an evening is
S1: with friends and family and smart people or whatever, and
S1: you're basically having conversation with actual humans. So to me,
S1: all this AI stuff, the whole purpose of it is
S1: to enhance your ability to do those things. And this
S1: is still this is all part of human 3.0. It's
S1: all part of like, what should we actually be doing?
S1: And what we should actually be doing is magnifying human experience.
S1: Slow experience. Right. All this speeding up and all this
S1: collection and all this, I should be in service of that,
S1: not in opposition to that. All right. That's how we
S1: get stuck on the first item. Not even getting into
S1: the newsletter. Okay. Yeah. Go sign up for the podcast again,
S1: which is silly because you're already listening to it. Uh,
S1: let's see here my friend Tracy Talbot is presenting Tell
S1: No Lies. Teaching AI to know when it doesn't know. Yeah,
S1: to know when it doesn't know. And this is June
S1: 26th in Austin, and she's got a LinkedIn post that
S1: I linked in the newsletter. I worked with her at Apple.
S1: She was extraordinary there at Apple. She was doing data quality, uh,
S1: on our team. And yeah, just really, really great person and, um,
S1: really thoughtful about data quality and data security and all
S1: these different aspects of data and basically data management at
S1: scale and thinking about what it means to trust data.
S1: So now she's getting into AI, and she's been doing
S1: a whole lot of really cool stuff with that. So
S1: definitely recommend going to see her talk again. That's June
S1: 26th in Austin. All right. Places I'm going to be
S1: speaking in 2025, and I realized I missed an item
S1: on the list. Various talks and panels. Blackhat USA in
S1: Vegas speaking at the Swiss Cyberstorm in Bern, Switzerland. It's
S1: going to be an AI workshop. Actually, I'm going to
S1: do a workshop for people, which is going to be
S1: a combination of like augmented fabric slash, essentially like personal
S1: AI infrastructure is kind of what that thing's going to
S1: be about. Keynoting Appsec USA in DC in November and
S1: speaking at Blackhat Mia in Saudi Arabia in December. And
S1: the one that I missed is I'm probably going to
S1: be doing something in Sweden as well. Um, I can't
S1: remember the exact date for that one, but, uh, yeah,
S1: I'll probably add that to the list for this next
S1: coming newsletter. On Monday, personal tech Stack updates I'm all
S1: in on cloud code. I'm not going to go into why,
S1: but ultimately it's because here I am going into why, uh,
S1: anthropic uses this as their agentic coding agent, and they've
S1: gone all in on Agentic coding, which means this thing
S1: isn't like hacking together agents. It's actually using agents to
S1: execute the various tasks. And as of today, I just
S1: updated cloud code and now it has planning built in.
S1: So as I've been talking about pretty much everywhere, I
S1: think scaffolding around AI is the superpower that's about to
S1: unlock this whole thing, because the models are already smart.
S1: The problem is they can't think for long. They have
S1: short memories. So we have all these context problems. So
S1: any any person, any intelligent being gets really stupid when
S1: it can't remember longer than, you know, a short conversation.
S1: And then you have to go back into this stored
S1: memory bank. And it's not perfect. I'm talking about rag,
S1: for example. Or if you have actual humans and they
S1: struggle with long term memory or even short term memory,
S1: obviously they're going to be less functional, right? They're going
S1: to be less capable than they used to be before
S1: they lost those abilities. So the ability to um, as
S1: Dwarkesh talks about, learn on the job. Okay. You think
S1: about a knowledge worker. They go in, they do orientation,
S1: they learn on the job. Okay. Guess what? After that
S1: first week, they're way better than when they started because
S1: now they have some knowledge. Well, what happens after six months?
S1: What happens after a year? What happens after they've been
S1: there for 20 years. They now have all this stored
S1: knowledge about this company, which is now part of their context.
S1: But the difference is with a human, they are naturally, fluidly.
S1: Every time they make a decision, they are thinking of
S1: their stored knowledge. They incorporate all of their decisions with
S1: their stored knowledge every time they make any decision. That
S1: is a thing that I currently cannot do. It is struggling.
S1: It's like, oh, I've got to go grab all this
S1: context and stuff it into the prompt. How do you
S1: grab context of 25 years of learning at a job?
S1: How do you know what that even means? How do
S1: you even know what to query to stuff into your
S1: decision making process? This is completely transparent with humans. When
S1: we are like, oh, let me approach this problem from
S1: a holistic standpoint. Let me talk to talk to all
S1: these disparate teams across the org. And I'm going to
S1: gather notes and I'm going to take notes. I'm going
S1: to write them down or whatever. Your brain is going
S1: crazy with all of its knowledge that you've gathered over
S1: these 25 years, and it's making its own queries and
S1: pulling back that context. And these are coming back to
S1: you as kind of like insights or thoughts or creativity.
S1: You're not getting to see exactly what the queries look
S1: like or even what the returned results look like. We
S1: do not have this with AI. We must manually go
S1: and make manual queries into context, like with rag or
S1: like with stuffing other context or context summaries into the
S1: prompts themselves, right? This is the scaffolding that we need.
S1: We need to emulate the process that humans execute naturally
S1: every day, you know, hundreds or thousands or millions of times.
S1: However many times it is that we're making decisions and
S1: little tiny, you know, nano decisions and micro decisions. So
S1: I don't remember how I got onto this, but scaffolding massively,
S1: massively important. And, uh, that's why I think context is,
S1: is so important to all of this stuff. Um. Oh,
S1: I remember how Claude Cote. So they are all in
S1: on the agent stuff, and they are all in on
S1: the planning component. Okay, this planning component is yet another
S1: piece of what I just talked about. It is, you know,
S1: keeping in mind what we're trying to do overall and
S1: then checking off little boxes of like, yeah, we've done this. Yeah,
S1: we've done this. It's all about, uh, context management. Now
S1: context is a little bit, um, overloaded here. I would
S1: say context planning management. Um, learning on the job management,
S1: long term task management. These are the types of things
S1: that really good knowledge workers, like a principle engineer who's
S1: been somewhere for 25 years. These are the types of
S1: things that they are really good at. And these are
S1: the things in my opinion, this is a prediction and
S1: this is just my prediction, is that we're going to
S1: see major advances in those areas, which are actually kind
S1: of like tricks. This is what I've been telling my friend, uh,
S1: Joel ever since 23, that these types of tricks are
S1: the things that are going to push us over the edge.
S1: These are the types of tricks that are going to
S1: hyper jump. I way further ahead. And I think what
S1: I think my theory has been borne out through things
S1: like Post-training post-training is not fundamentally better neural nets. It's
S1: better ways of shaping and getting neural nets to do
S1: what we actually want them to do. Right. So just
S1: zooming out, I think Cloud Code has this figured out.
S1: I think anthropic really deeply understands this. And they are
S1: building agents and planning management and context Management. They're doing really,
S1: really well with it in a way that I'm just
S1: really excited to build things with cloud code in a
S1: way that's not really hitting for me. Uh, on the
S1: other platforms, my secondary backup for cloud code is still cursor. Um,
S1: I was messing with, uh, windsurf for a while and
S1: some other tools, and I still dabble with all the
S1: other tools when I see something, uh, come out or new,
S1: I go and tinker with it. But right now, cloud
S1: code is my main, with cursor being secondary. I'm also
S1: switched over to using Dia as my main daily driver
S1: for my browser. And um, I'm on all the Mac
S1: OS 26 betas. They are fantastic. And uh, specifically having
S1: your phone app on the desktop is fantastic. And, uh,
S1: the podcast app is much improved. You can actually go
S1: above the two x limit. You can go up to
S1: three x now. And yeah, overall I would say Apple
S1: just really did a lot of quality updates. For example
S1: AirPod handoffs are much better. So that's it for updates
S1: I think. And let's get into cybersecurity. Apple quietly fixed
S1: an iPhone zero Day that was used against journalists. Echo
S1: leak using uses markdown syntax to bypass Microsoft 365 Copilot security.
S1: So some researchers found a smart way to steal data
S1: from Copilot by using obscure markdown link formats that Microsoft
S1: did not filter. Researchers turn 2 a.m. Tokyo hotel room
S1: chat into a Netflix RC. So Chubbs, who is a
S1: friend of mine and very, very good, um, attacker, uh, attacker. Um,
S1: what are we saying? Researcher. Bug. Bounty guy. Um. Security
S1: guy focused on offensive security and automated testing. He's also
S1: the creator of Asset note. So he's just one of
S1: the top, you know, top 1% or 1% of the 1%.
S1: I would put him in Sam Curry like up in
S1: that top, top echelon. And, uh, basically they found, uh,
S1: the way they found a way to attack an unclaimed
S1: internal package name called NFC logger. And they use that
S1: to get Archie actually on a Netflix, uh, system or
S1: within Netflix itself. And I'm sure they fixed that. Otherwise
S1: we wouldn't be talking about it. All right. Going to
S1: skip over the Tliose sessions. Uh, offer. That was in
S1: the thing. Uh, don't really want to talk about that. Um,
S1: HTML spec now escapes angle brackets and attributes. Tributes, so
S1: Chrome and other browsers are now going to start escaping
S1: left and right. Caret characters in HTML attributes to prevent
S1: mutation XSS attacks Interpol dismantles 20,000 malicious IPS linked to
S1: 69 malware variants. Cursor security rules project tackles unsafe AI
S1: generated code, Europol says Europol yeah, Europol says stolen data
S1: has become the new underground currency. Phishing messages this is
S1: a quote created by Llms have higher success rate than
S1: those written by humans. And this is a euro pool.
S1: Io CTA report US airlines quietly selling flight data to DHS. Yeah.
S1: Government agencies can search 39 months of flight data, including
S1: passenger names, itineraries, travel dates and credit card numbers, and
S1: other people buying this data are include like the Secret Service, SEC, DEA,
S1: and US Marshals Service. And that's in addition to the
S1: DHS and all its various services. Amazon's AI agents can
S1: build cyber defense signatures in minutes. National security thoughts on
S1: Israel's action against Iran. I'm actually not going to go
S1: super into this one. It's an evolving situation. As everyone knows,
S1: new stuff is happening like every few hours on this, uh, go,
S1: go read the newsletter if you want to see my
S1: take on this. Army is commissioning Big Tech executives as
S1: lieutenant colonel. So for Silicon Valley executives from meta, Palantir
S1: and OpenAI are being brought in as lieutenant colonels to
S1: speed up the government's adoption of technology. And I'm overall
S1: for this, but I also have my spider senses going
S1: off in terms of like dystopia type situation. Again, I,
S1: I see the value of Palantir. I see the value
S1: of winning at almost any cost against China. Right? That
S1: is why I support this type of move from the
S1: military and why I support overall. In some ways, companies
S1: like Palantir and Andriel and you know why these companies exist? Yes,
S1: we need to move super fast. Unfortunately, the cultures and
S1: the ambitions and a whole bunch of externalities often come
S1: with moving really fast and in really ambitious ways. In
S1: with technology companies where you're making tons of money supporting
S1: the military. I don't think I need to spell it out.
S1: There's possibility for harm here, right? There's possibility for harm here.
S1: And I think we have to take that risk. I
S1: think this is the right move to take that risk.
S1: But holy crap, is it scary? Yes. And do we
S1: need oversight? Yes. Do we need to be extremely careful? Yes.
S1: Do we need to not turn our eyes away and
S1: just hope it all works out? Absolutely. Cheap drones will
S1: massively disrupt current large military dominance. Yeah. Israel and Ukraine
S1: are showing that $500 drones can destroy extremely expensive military gear,
S1: which could upend the advantage of countries like, you know, Russia,
S1: the US. We're already seeing it with Russia in Ukraine. Uh,
S1: big scope aside on this one. I'm curious about how
S1: this is going to start trickling into consumer and personal security.
S1: Like when will executive production teams all need to have
S1: like Anti-drone tech as part of their package? I think
S1: probably sooner rather than later. I that apple paper saying
S1: eyes don't reason was highly flawed, and someone named Alex
S1: Lawson says that the paper basically didn't do a good
S1: job and it was easy to make better examples. Although
S1: I've heard someone did a paper like this, this might
S1: have been Alex Lawson. Actually, I don't know if it
S1: was this paper. I heard somebody say that that whole paper,
S1: this whole takedown paper was actually Claude generated, and it
S1: wasn't a real paper. And it was just like, I
S1: don't know, fake or hype or whatever, and that they
S1: didn't expect it to blow up so much. I don't
S1: know if it was this one, but interesting to note.
S1: Also interesting to note, and I'm going to beat up
S1: on myself here for a second. I don't know if
S1: this was the paper that said that it doesn't actually
S1: matter if I know, or it doesn't actually matter if
S1: it was the paper or not. The fact that I
S1: put it in the newsletter is potentially a strike against me.
S1: Think about this. I counted as a strike against myself. Um,
S1: the thing is, I'm not sure if I would do
S1: it differently. It's just maybe I, I don't know. This
S1: is weird. Okay. Because when I put something in the
S1: newsletter and I talk about it, how much of a
S1: high how much have I endorsed this thing and said
S1: for sure that this is a real paper? For sure.
S1: These are real results because I read the paper fully.
S1: Not only that, but I looked at the all the
S1: testing methodologies and I evaluated the data and I confirmed
S1: that their testing was not a lie. That is a
S1: level of depth that I cannot do. For the amount
S1: of papers and stories that I'm doing. So what I'm
S1: saying is it is possible for me to be duped. Um,
S1: and this one very well could be, uh, in fact,
S1: you know what? I'm going to click on a link
S1: here and see if there's possible, like updates. I don't
S1: think so. I don't think so. I don't think it applied.
S1: It doesn't actually matter if it applied. This paper that
S1: I included might be completely legit. And I think it is,
S1: but it doesn't actually matter because this strike against me
S1: still counts, right? So this is a question from me
S1: to myself and also from you to me and from
S1: me to you, to me. You got to watch what
S1: people are posting, and you got to watch with how
S1: sure that they are that that thing is actually real
S1: And whether or not they're just using something that sounds
S1: real to support their own opinion. This one happened to
S1: go with my opinion. Oh, so what do you know? Magically,
S1: I just included in the newsletter as support for my opinion. Well,
S1: what if it turns out there was complete garbage? Well
S1: then I'm just like anyone else who is posting a
S1: random garbage. I would argue I'm not like that, but
S1: I'm saying it looks kind of the same, so I
S1: don't really care how much the difference is. So just
S1: keep in mind. And not just for me, but for yourself.
S1: You got to watch for whether or not evidence that's
S1: coming in is only air quote evidence, because how much
S1: time have you actually spent looking at the evidence, redoing
S1: the paper yourself, you know. And so how much are
S1: we using external validation or citations pointing to a paper
S1: Or as support for the paper being legit. And I
S1: think it gets more and more serious the further we go. Um,
S1: the good news is, a big part of my personal
S1: AI structure is going to be to find stuff like this. Uh,
S1: I want to be able to find stuff like this
S1: with my AI infrastructure where it's like, I don't know,
S1: seems like they use a lot of, uh, catchy AI
S1: terms in here, and it looks like they were really
S1: hand-wavy with their examples. Like, I want to be able
S1: to catch this stuff for myself. Just something to keep
S1: in mind. OpenAI's O3 Pro is very smart, but context hungry. Agreed.
S1: Man killed by police after spiraling into a ChatGPT driven psychosis.
S1: Google plans to cut ties with scale AI because basically
S1: meta bought like half of them or something. CIO wants
S1: to clone staff as digital twins and AI agents. UC
S1: San Diego CIO wants to extract knowledge from experienced IT
S1: staff and create digital twins that handle routine problems. Well, yeah, obviously,
S1: you know, you got somebody, uh, greybeard, you know, Chris Myers,
S1: who's been at the company for 41 years, and they
S1: know everything. And anyone who, you know, finds something broken
S1: that nobody could fix, they all call Chris Myers. So
S1: the CEO is like, let's go find Chris Myers. Let's
S1: go interview him for seven weeks in a dark, padded
S1: room and pull out all of his knowledge and put
S1: that into an AI agent. And now you could just
S1: ask a virtual Chris Myers. Well, obviously we need this
S1: for everything, right? We need this for the best doctors.
S1: We need this for the best psychiatrists. We need this
S1: for everything. This is you know, this is the problem. When,
S1: you know, extremely experienced people leave a company, you lose
S1: all the knowledge that they, uh, took with them. And
S1: unfortunately also for I, it's also knowledge that you can't
S1: possibly extract from them efficiently, right? You can't possibly ask
S1: enough questions to to gather their knowledge. Their knowledge is,
S1: let's call it infinite. 41 years of knowledge essentially baked
S1: into an AI model. That's a great analogy actually it.
S1: How many questions do you have to ask? You know, uh,
S1: O3 to get all of O3 knowledge out of it?
S1: The answer is an infinite number of questions. So it
S1: doesn't matter how much you interview Chris Myers, who's been
S1: there for 41 years, you're not going to get enough. Right?
S1: So so this is a serious challenge. ChatGPT dominates LLM
S1: usage at 80%, 86% market share. I did not know
S1: it was that high technology fabric. Summary of the massive
S1: Google outage. Yeah. This is this is really cool. Um,
S1: so they put out the, you know, Google put out
S1: their their giant paper saying what happened. And when I see,
S1: you know, a 14 page paper or whatever. And it's
S1: about an outage and describing an outage, it's not like
S1: deep human knowledge that I'm getting. That's where I run Extraction's.
S1: So I've got this, um, pattern called create story explanation,
S1: create underscore story story underscore explanation. It is one of
S1: my favorite patterns. It explains anything at like a ninth
S1: grade level in kind of a story format. So this happened,
S1: then this happened, then this happened. And it's just very clear.
S1: So I recommend checking it out if you care about
S1: that outage, but more importantly using it against other stuff.
S1: YouTube officially beats all other streaming platforms. In US viewership,
S1: they have 13% of all US TV viewership. Oh, wow. Okay.
S1: I thought it was 13% of streaming. Okay, I read
S1: that wrong. 13% of all US TV viewership. That's insane.
S1: That's insane. Okay, YouTube by itself, 13% of TV. Yeah,
S1: it seems very high. I bet you that goes up
S1: crazy amounts in the next few years. It's basically the
S1: only TV that I watched. I was actually at a
S1: resort yesterday in Napa, and they only had regular TV,
S1: and it was like it was like a commercial rectangle.
S1: All I did was watch commercials and occasionally they would
S1: show like part of a show. Anyway, Amazon joins the
S1: big nuclear party buying 1.2GW, uh, for AWS. tvOS 26
S1: hints at built in camera for a new Apple TV 4K.
S1: I wish it was an Apple TV 8-K, and I
S1: wish it had ten Gigabit Ethernet. That's what I wish.
S1: But I would like to see a camera because I'd
S1: like to do FaceTime with, um, in the living room
S1: sitting on the couch. That would be nice. I also
S1: don't want a camera facing the living room, so I
S1: would need one of those flip dongle cover things. Apple's
S1: iPad OS 26 finally makes the iPad a real computer.
S1: Everyone is talking about this. People are saying that this
S1: actually allows you to use an iPad as a laptop,
S1: almost like very closely. And it's kind of been universal
S1: that people are saying, Holy crap, this is really, really good.
S1: I have not. I don't use my iPads much. And
S1: the reason is I just didn't like the OS. The
S1: only thing that I use it for is reading and
S1: drawing and sketching and doing stuff like that with like freeform.
S1: And for that I really love it. And for reading,
S1: I really love it. But I don't use it as
S1: a computer because I prefer laptops. Maybe that will change
S1: the argument that it's time to kill Siri. I did
S1: not know that I believe this, but I definitely do.
S1: I think once Apple fixes its AI interface, its universal
S1: AI to life OS that I've been talking about, they
S1: need to call it something new. Siri has too much baggage. I.
S2: I can show them if you ask.
S1: Hey Siri, stop the one time Siri actually listens when
S1: I'm trying to record, um, it essentially that system does
S1: not turn on and turn off lights reliably. It has
S1: so many problems. It's been over ten years. It had
S1: its attempt. It had its chances, and now it needs
S1: a new name. I thoroughly believe this. It's just a
S1: marketing concept. When you have enough bad baggage associated with something,
S1: you got to flip it over. You got to retire it.
S1: Nvidia writes off China revenue in company forecasts because, you
S1: know it's too unpredictable. With the current administration, Waymo rides
S1: cost more than Uber or Lyft, but people are happy
S1: paying it so often 5 or $10 more for a
S1: ride versus like Uber or a taxi. But people are
S1: still paying it and they're still making money because it's
S1: so much of a better experience. Humans. Trump ends protection
S1: for Afghans as Congress scrambles to intervene. I'm really upset
S1: about this, really upset that these Afghanis who had helped
S1: the US military during the war and who cannot go
S1: back because they are being hunted by the Taliban and
S1: because their families are under like watch by the Taliban,
S1: they are in grave danger. Their families are and they are.
S1: And if they go back and try to link with
S1: their families because they're forcibly kicked out of the US,
S1: it is essentially damning them and their families, potentially to die.
S1: And this is after they served and sacrificed essentially for
S1: the US military. I do not believe we should treat
S1: allies like that. Robin I system makes first autonomous scientific discovery.
S1: So this is future houses. Robin I so I talked
S1: about this story of automated, you know, science assistance. And
S1: this is even more it's more end to end the
S1: entire research process. So it, um, it discovered that ripasudil
S1: could treat dry macular degeneration by orchestrating multiple specialized agents
S1: to handle the entire research process autonomously in just 2.5 months.
S1: And who knows how much of that is like the
S1: entire air quotes entire research process, but supposedly it went
S1: end to end and actually worked. So this is the
S1: type of thing that I think is really, really exciting.
S1: Ultra black paint might solve satellite light pollution crisis. Although
S1: I'm curious, wouldn't you just see black dots moving through
S1: your pictures, blocking out stars and, you know, the moon
S1: or whatever? I guess the black dots wouldn't be as annoying.
S1: It seems like they could still overwrite stars. If you
S1: are trying to take a picture of the starry sky,
S1: you might still see artifacts, but it won't be nearly
S1: as bad as like a bright white line Him drawing
S1: over your picture, actually multiple bright white lines. But, uh,
S1: I wonder how possible this is, like, are we going
S1: to pull all the ones that are up there down?
S1: Are we going to paint the ones that are up there? Doubtful.
S1: So this would involve painting all the new ones that
S1: are going up, which unfortunately is many thousands. And the
S1: Pentagon has evidently been pushing Americans to believe in UFOs
S1: for decades. And evidently, according to this report, this is
S1: to cover up actual secret weapons programs. So they basically
S1: circulate this stuff on purpose to get people talking about
S1: a red herring discovery. Fiddle ITM brings malicious traffic detection
S1: to man of the middle proxy. Oh, I'm not going
S1: to actually read all of these, so I'm just going
S1: to scroll through and look for some really cool ones,
S1: or if any are from my friends. Let's see. Boom
S1: boom boom boom. Oh, Sam Altman's gentle singularity. You probably
S1: want to read this one. I've also got a fabric
S1: extracted predictions that comes out of it in the newsletter,
S1: so check that out. And yeah, I think that's about it. Okay,
S1: this is the end of the standard edition of the podcast,
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S1: over to Daniel Music.com. That's Daniel Miessler, and we'll see
S1: you next time.